本文实例为大家分享了python将两张图片生成全景图片的具体代码，供大家参考，具体内容如下

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1、全景图片的介绍

全景图通过广角的表现手段以及绘画、相片、视频、三维模型等形式，尽可能多表现出周围的环境。360全景，即通过对专业相机捕捉整个场景的图像信息或者使用建模软件渲染过后的图片，使用软件进行图片拼合，并用专门的播放器进行播放，即将平面照片或者计算机建模图片变为360 度全观，用于虚拟现实浏览，把二维的平面图模拟成真实的三维空间，呈现给观赏者。

2、如何实现

2.1、实现原理

主要是利用sift的特征提取与匹配，参考链接

2.2、实现代码

# -*- coding:utf-8 -*-
u'''
Created on 2019年6月14日
@author: wuluo
'''
__author__ = 'wuluo'
__version__ = '1.0.0'
__company__ = u'重庆交大'
__updated__ = '2019-06-14'
import numpy as np
import cv2 as cv
from PIL import Image
from matplotlib import pyplot as plt
print('cv version: ', cv.__version__)

def pinjie():
 top, bot, left, right = 100, 100, 0, 500
 img1 = cv.imread('G:/2018and2019two/qianrushi/wuluo1.png')
 cv.imshow("img1", img1)
 img2 = cv.imread('G:/2018and2019two/qianrushi/wuluo2.png')
 cv.imshow("img2", img2)
 srcImg = cv.copyMakeBorder(
  img1, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
 testImg = cv.copyMakeBorder(
  img2, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
 img1gray = cv.cvtColor(srcImg, cv.COLOR_BGR2GRAY)
 img2gray = cv.cvtColor(testImg, cv.COLOR_BGR2GRAY)
 sift = cv.xfeatures2d_SIFT().create()
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1gray, None)
 kp2, des2 = sift.detectAndCompute(img2gray, None)
 # FLANN parameters
 FLANN_INDEX_KDTREE = 1
 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
 search_params = dict(checks=50)
 flann = cv.FlannBasedMatcher(index_params, search_params)
 matches = flann.knnMatch(des1, des2, k=2)
 
 # Need to draw only good matches, so create a mask
 matchesMask = [[0, 0] for i in range(len(matches))]

 good = []
 pts1 = []
 pts2 = []
 # ratio test as per Lowe's paper
 for i, (m, n) in enumerate(matches):
  if m.distance < 0.7 * n.distance:
   good.append(m)
   pts2.append(kp2[m.trainIdx].pt)
   pts1.append(kp1[m.queryIdx].pt)
   matchesMask[i] = [1, 0]

 draw_params = dict(matchColor=(0, 255, 0),
      singlePointColor=(255, 0, 0),
      matchesMask=matchesMask,
      flags=0)
 img3 = cv.drawMatchesKnn(img1gray, kp1, img2gray,
        kp2, matches, None, **draw_params)
 #plt.imshow(img3, ), plt.show()

 rows, cols = srcImg.shape[:2]
 MIN_MATCH_COUNT = 10
 if len(good) > MIN_MATCH_COUNT:
  src_pts = np.float32(
   [kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
  dst_pts = np.float32(
   [kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
  M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)
  warpImg = cv.warpPerspective(testImg, np.array(
   M), (testImg.shape[1], testImg.shape[0]), flags=cv.WARP_INVERSE_MAP)

  for col in range(0, cols):
   if srcImg[:, col].any() and warpImg[:, col].any():
    left = col
    break
  for col in range(cols - 1, 0, -1):
   if srcImg[:, col].any() and warpImg[:, col].any():
    right = col
    break

  res = np.zeros([rows, cols, 3], np.uint8)
  for row in range(0, rows):
   for col in range(0, cols):
    if not srcImg[row, col].any():
     res[row, col] = warpImg[row, col]
    elif not warpImg[row, col].any():
     res[row, col] = srcImg[row, col]
    else:
     srcImgLen = float(abs(col - left))
     testImgLen = float(abs(col - right))
     alpha = srcImgLen / (srcImgLen + testImgLen)
     res[row, col] = np.clip(
      srcImg[row, col] * (1 - alpha) + warpImg[row, col] * alpha, 0, 255)

  # opencv is bgr, matplotlib is rgb
  res = cv.cvtColor(res, cv.COLOR_BGR2RGB)
  # show the result
  plt.figure()
  plt.imshow(res)
  plt.show()
 else:
  print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
  matchesMask = None

if __name__ == "__main__":
 pinjie()

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